MetNet-2's forecasts have a spatial resolution of 1 km \(\times \)  1 km. The state-of-the-art NWP model High-Resolution Rapid Refresh (HRRR) [1]} that is operational for the CONUS region makes hourly forecasts at a 3 km \(\times \)  3 km resolution. According to a diverse set of standard evaluation metrics, MetNet-2 outperforms the NWP model at predicting precipitation up to 12 hours of lead time without physics-based atmospheric forecasts. MetNet-2 is even more naturally compared with Ensemble NWP models due to the latter producing probabilistic forecasts similar to MetNet-2's own forecasts. A state-of-the-art physics-based ensemble model is the High Resolution Ensemble Forecast (HREF) [2]} that also operates in the CONUS region at a 3 km \(\times \)  3 km resolution. Here too, MetNet-2 is able to outperform HREF up to 12 hours of lead time at predicting a cumulative rate of precipitation. Further results also indicate that, coupled with the forecast of the HRRR model, MetNet-2 is able to substantially improve HRRR's forecast with post-processing and hybrid modes. These results hold across a broad range of precipitation rates, from low to high.
